The interactions of cell surface membrane components play crucial roles in transmitting extracellular signals to a cell in normal physiology, and in disease conditions. Many types of cell surface receptors undergo dimerization, oligomerization, or clustering in connection with the transduction of an extracellular event or signal, e.g. ligand-receptor binding, into a cellular response, such as proliferation, increased or decreased gene expression, or the like, e.g. George et al, Nature Reviews Drug Discovery, 1: 808-820 (2002); Mellado et al, Ann. Rev. Immunol., 19: 397421 (2001); Schlessinger, Cell, 103: 211-225 (2000); Yarden, Eur. J. Cancer, 37: S3-S8 (2001). The role of such signal transduction events in diseases, such as cancer, has been the object of intense research and has led to the development of several new drugs and drug candidates, e.g. Herbst and Shin, Cancer, 94: 1593-1611 (2002); Yarden and Sliwkowski, Nature Reviews Molecular Cell Biology, 2: 127-137 (2001); McCormick, Trends in Cell Biology, 9: 53-56 (1999); Blume-Jensen and Hunter, Nature, 411: 355-365 (2001).
Many disease states are characterized by differences in the expression levels of various genes either through changes in the copy number of the genetic DNA or through changes in levels of transcription of particular genes (e.g., through control of initiation, provision of RNA precursors, RNA processing, etc.). Dynamic changes in the nucleosomal packaging of DNA must occur to allow transcriptional proteins to contact with the DNA template. One of the most important mechanisms influencing chromatin remodeling and gene transcription are the posttranslational modification of histones and other cellular proteins by acetylation and subsequent changes in chromatin structure (Davie, 1998, Curr Opin Genet Dev 8, 173-8; Kouzarides, 1999, Curr Opin Genet Dev 9, 40-8; Strahl and Allis, 2000, Nature 403, 41-4).
Histones are small, positively charged proteins which are rich in basic amino acids (positively charged at physiological pH), which contact the phosphate groups (negatively charged at physiological pH) of DNA. The majority of histones are synthesized during the S phase of the cell cycle, and newly synthesized histones quickly enter the nucleus to become associated with DNA. Within minutes of its synthesis, new DNA becomes associated with histones in nucleosomal structures.
There are five main classes of histones, H1, H2A, H2B, H3, and H4. The amino acid sequences of histones H2A, H2B, H3, and H4 show remarkable conservation between species, whereas H1 varies somewhat, and in some cases is replaced by another histone, e.g., H5. Four pairs of each of H2A, H2B, H3, and H4 together form a disk-shaped octomeric protein core, around which DNA (about 140 base pairs) is wound to form a nucleosome. Individual nucleosomes are connected by short stretches of linker DNA associated with another histone molecule (e.g., H1, or in certain cases, H5) to form a structure resembling a beaded string, which is itself arranged in a helical stack, known as a solenoid.
Briefly, acetylation neutralizes the positive charge of the lysine side chain, and is thought to impact chromatin structure. Hyperacetylation of the N-terminal tails of histones H3 and H4 correlates with gene activation whereas deacetylation can mediate transcriptional repression. When this occurs in genes critical to growth inhibition, the resulting silencing of transcription could promote tumor progression.
Specifically, in the case of histone hyperacetylation, changes in electrostatic attraction for DNA and steric hindrance introduced by the hydrophobic acetyl group leads to destabilization of the interaction of histones with DNA. As a result, acetylation of histones disrupts nucleosomes and allows the DNA to become accessible to the transcriptional machinery. Removal of the acetyl groups allows the histones to bind more tightly to DNA and to adjacent nucleosomes and thus to maintain a transcriptionally repressed chromatin structure. Consequently, STAT expression correlates with transcriptional activation, whereas histone deacetylation is associated with gene repression.
Acetylation is mediated by a series of enzymes with histone acetyltransferase (HAT) activity. Conversely, acetyl groups are removed by specific histone deacetylase (HDAC) enzymes whose deregulation is associated with several cancers. Disruption of these mechanisms gives rise to transcriptional misregulation and may lead to tumorigenic transformation. In addition, other molecules such as transcription factors alter their activity and stability depending on their acetylation status. The recruitment of histone acetyltransferases (HATs) and histone deacetylases (HDACs) is considered as a key element in the dynamic regulation of many genes playing important roles in cellular proliferation and differentiation. Defects in both HATs and HDACs have been reported in a variety of cancers. See Kouzarides, T., “Histone acetylases and deacetylases in cell proliferation,” Curr. Opin. Genet. Dev., 9: 40-48 (1999) for an excellent review.
A growing number of histone deacetylases (HDACs) have been identified. See, for example, Ng, H. H. and Bird, A., “Histone deacetylases: silencers for hire.” Trends Biochem. Soc., vol. 25:121-126 (2000). Mammalian histone deacetylases can be divided into three subclasses (see, for example, Gray and Ekstrom, Exp Cell Res, January 15; 262(2):75-83 (2001)). HDACs 1, 2, 3, and 8 which are homologues of the yeast RPD3 protein constitute class I. HDACs 4, 5, 6, 7, 9, and 10 are related to the yeast Hda 1 protein and form class II. Recently, several mammalian homologues of the yeast Sir2 protein have been identified forming a third class of deacetylases which are NAD dependent. All of these HDACs appear to exist in the cell as subunits of a plethora of multiprotein complexes. In particular, class I and II HDACs have been shown to interact with transcriptional corepressors, mainly N-CoR and SMMT, which serve as bridging factors required for the recruitment of HDACs to transcription factors.
Since histone deacetylases (HDACs) are involved in cell cycle progression and differentiation, and their deregulation is associated with several cancers, recent efforts have focused on identifying potent HDAC inhibitors (HDACi). Recently, certain compounds that induce terminal differentiation have been reported to inhibit histone deacetylases. Indeed, suberoylanilide hydroxamic acid (SAHA) is a potent inhibitor of HDACs that causes growth arrest, differentiation, and/or apoptosis of many tumor types in vitro and in vivo. Because of its low toxicity, SAHA is currently in clinical trials for the treatment of cancer. SAHA is reported to be effective in preventing the formation of mammary tumors in rats, and lung tumors in mice.
Cancer diseases account for nearly one-quarter of deaths in the United States, exceeded only by heart diseases. The disease contributes to a major financial burden to the community and to individuals. A central paradigm in the care and treatment of patients presenting with cellular proliferative disorders mediated by HDAC(HDAC+) to offer better risk assessment, screening, diagnosis, prognosis and selection and monitoring of therapy. At present, cancer patients often undergo chemotherapy and radiotherapy. However, the treatment outcome is not always satisfactory.
In the early clinical development of anti-cancer agents, clinical trials are typically designed to evaluate the safety tolerability, and pharmacokinetics, as well as to identify a suitable dose and schedule for further clinical evaluation. Scientists believe that the development of new validated intermediate end points will lead to significant reductions in healthcare and drug development costs as well as provide a tool for achieving successful preventive intervention. Increasingly, efforts are being expended towards identifying high-risk individuals who are at risk of, or susceptible to, becoming resistant to a particular therapeutic moiety or alternatively, not responding to a particular therapeutic moiety. Earlier identification of such at-risk patients would help in the development of molecular-targeted interventions to prevent or delay neoplasia. Mindful that prognosis and prediction of response are necessary for the selection of neoadjuvant or adjuvant chemotherapy, it would be useful to be able to identify clinically relevant intermediate end points, which may predict not only the final outcome of a chemopreventive trial but also help identify high-risk patients. After all, avoiding ineffective therapies is as important as identifying effective ones.
As a consequence, a great deal of effort is being directed to using new technologies to find new classes of biomarkers, which is becoming one of the highly prized targets of cancer research. See Petricoin et al, Nature Reviews Drug Discovery, 1: 683-695 (2002); Sidransky, Nature Reviews Cancer, 2: 210-219 (2002). Overall, risk biomarkers will find use not only in diagnosis but also predict response to therapy, identify potential candidates who may best be suited for a particular chemopreventive intervention, aid in the rational design of future intervention therapy. The study of biomarkers that can possibly predict how a person's disease may progress or respond to treatment, falls under the category of chemoprevention. Biomarkers used to measure a response to an intervention are called surrogate endpoint biomarkers or SEBs (Kelloff et al. Cancer Epidemiology, Biomarkers and Prev., 5: 355-360 (1996). Examples of biomarkers include genetic markers (e.g., nuclear aberrations [such as micronuclei], gene amplification, and mutation), cellular markers (e.g., differentiation markers and measures of proliferation, such as thymidine labeling index), histologic markers (e.g., premalignant lesions, such as leukoplakia and colonic polyps), and biochemical and pharmacologic markers (e.g., ornithine decarboxylase activity).
The identification of these biomarkers may be carried out by analyzing changes in specific polypeptides or mRNA, as predicted by the known biology associated with the molecule targeted by the agent of interest. Alternatively, biomarkers can be identified by analyzing global changes in polypeptides or mRNA in cells or tissues exposed to efficacious doses of the agent. Once identified, these biomarkers can be used to tailor a patient's clinical protocol such as, for example, being able to predict a patient's response to a particular treatment protocol with a particular therapeutic moiety.
Current predictive and prognostic biomarkers include DNA ploidy, S-phase, Ki-67, Her2/neu (c-erb B-2), p53, p21, the retinoblastoma (Rb) gene, MDR1, bcl-2, cell adhesion molecules, blood group antigens, tumor associated antigens, proliferating antigens, oncogenes, peptide growth factors and their receptors, tumor angiogenesis and angiogenesis inhibitors, and cell cycle regulatory proteins. Beta human chorionic gonadotropin (β-hCG), carcinoembryonic antigen, CA-125, CA 19-9, and others have been evaluated and shown to correlate with clinical response to chemotherapy. See de Vere White R. W., Stapp E, “Predicting prognosis in patients with superficial bladder cancer” Oncology(Hunting), 12(12):1717-23; discussion 1724-6 (1998); Stein J P et al., “Prognostic markers in bladder cancer: a contemporary review of the literature” J. Urol.; 160 (3 Pt 1):645-59 (1998); Cook A M et al., “The utility of tumour markers in assessing the response to chemotherapy in advanced bladder cancer” Proc. Annu. Meet. Am. Soc. Clin. Oncol., 17:1199 (1998).
In the case of cancer, molecular markers such as the level of HER2/neu, p53, BCL-2 and estrogen/progesterone receptor expression have been clearly shown to correlate with disease status and progression. This example demonstrates the value of diagnostic and prognostic markers in cancer therapy. Reports from retrospective studies have shown that multivariate predictive models combining existing tumor markers improve cancer detection. See van Haaften-Day C, Shen Y, Xu F, et al., “OVX1, macrophage-colony stimulating factor, and CA-125-II as tumor markers for epithelial ovarian carcinoma: a critical appraisal.”, Cancer (Phila), 92: 2837-44, (2001).
Notwithstanding the above references, the scientific literature is innocently silent of any teachings about prognostic biomarkers useful for tailoring a therapeutic protocol involving an HDAC-inhibitor.
The present invention aims at overcoming the above deficiencies by providing clinically relevant prognostic tools that may be used to identify a patient at risk of failing a therapeutic regiment involving a particular HDAC inhibitor, e.g., SAHA. Towards this end, the present invention describes for the first time a link between STAT protein expression and/or activation status (hyper-vs. hypo-phosphorylation patterns) and clinical response to SAHA. That is, it has been demonstrated in the examples appearing hereunder that the expression profiles of at least one of STAT-1, -3 and -5, individually or collectively, is predictive of the patient's response to treatment with SAHA.